Method and apparatus for characterizing and estimating the parameters of histological and physiological biometric markers for authentication

ABSTRACT

A plurality of electronic signals corresponding to a histological and/or physiological marker, such as a heartbeat, are obtained, from an individual and are converted into electronic signal form. The signals are measured to obtain an actual measurement of a plurality of variable features of the electronic signals relating to the heartbeats. The measurements are mathematically analyzed to provide the probability of divergence of each actual measurement. Using the calculated probability of divergence, subsequent received waveforms measurements are analyzed for authentication purposes.

CROSS-REFERENCE TO RELATED APPLICATIONS

Not applicable.

STATEMENT REGARDING FEDERALLY SPONSORED RESEARCH OR DEVELOPMENT

Not applicable.

INCORPORATION-BY-REFERENCE OF MATERIAL SUBMITTED ON A COMPACT DISC

Not applicable.

BACKGROUND OF THE INVENTION

1. Field of the Invention

The present invention relates to a method and apparatus forcharacterizing and estimating the parameters of a person's heartbeat forthe purpose of authenticating a biometric signal. More specifically, thepresent invention relates to methods and apparatus for characterizingand estimating the parameters of a heartbeat signal that issubstantially unique to a person in order to permit the person to usethe heartbeat signal as a biometric marker to activate a device,participate in a transaction, or identify him or herself.

2. Description of Related Art

The computer industry has recognized a growing need for sophisticatedsecurity systems for computer and electronic devices. The securitysystems are used to prevent unauthorized use and authenticate oridentify individuals through electronic means. The biometricauthentication industry has developed in response to this need.Biometrics is the measurement of quantifiable biological traits. Certainbiological traits, such as the unique characteristics of each person'sfingerprint, have been measured and compared and found to be unique orsubstantially unique for each person. These traits are referred to asbiometric markers. The computer and electronics industry is developingidentification and authentication means that measure and compare certainbiometric markers with the intention of using the markers as biological“keys” or “passwords.”

Biometric markers presently used by the industry for authentication andidentification include the use of measurements of unique visiblefeatures such as fingerprints, hand and face geometry, and retinal andiris patterns, as well as the measurement of unique behavioral responsessuch as the recognition of vocal patterns and the analysis of handmovements. The use of each of these biometric markers requires a deviceto make the biological measurement and process it in electronic form.The device may measure and compare the unique spacing of the features ofa person's face or hand and compare the measured value with a valuestored in the device's memory. Where the values match, the person isidentified or authorized.

Several types of technologies are used in biometric identification ofsuperficial anatomical traits. For example, biometric fingerprintidentification systems may require the individual being identified toplace their finger on a visual scanner. The scanner reflects light offof the person's finger and records the way the light is reflected off ofthe ridges that make up the fingerprint. Hand and face identificationsystems use scanners or cameras to detect the relative anatomicalstructure and geometry of the person's face or hand. Differenttechnologies are used for biometric authentication using the person'seye. For retinal scans, a person will place their eye close to or upon aretinal scanning device. The scanning device will scan the retina toform an electronic version of the unique blood vessel pattern in theretina. An iris scan records the unique contrasting patterns of aperson's iris.

Other types of technologies are used for biometric identification ofbehavioral traits. Voice recognition systems generally use a telephoneor microphone to record the voice pattern of the user received. Usuallythe user will repeat a standard phrase, and the device compares themeasured voice pattern to a voice pattern stored in the system.Signature authentication is a more sophisticated approach to theuniversal use of signatures as authentication. Biometric signatureverification not only makes a record of the pattern of the contactbetween the writing utensil and the recording device, but also measuresand compares the speed of the writing and pressure applied in theprocess of writing.

Each of the prior art systems has a number of disadvantages. Forexample, fingerprint databases may raise significant privacy issues forthose whose information is entered in the system. Hand and facialgeometry recognition systems may require large scanners and/or expensivecameras. Voice recognition devices have problems screening outbackground noise. Signature recognition devices are subject tovariations in the behavior of the individual. Retinal devices mayrequire users to place their eye close to or on a scanning device,exposing the user to potential infection.

Another disadvantage of the prior art to biometric authentication isthat there are only a limited number of biometric markers that arepractical for implementing in computer and electronic devices. Biometricpatterns used in the prior art to authenticate a person that arecompletely unique to each person may have only minute differences; thepatterns that distinguish one person from another person may be subtle.Measuring and authenticating such patterns may require a high degree ofelectronic sophistication to read and differentiate between the variousunique aspects of the biometric marker. If the biometric marker is usedto identify an individual from a large group of individuals, thecomputer memory storage and processing capability may also have to besophisticated, and therefore, may be expensive.

Another disadvantage of prior art is that with relatively few trulyunique biometric markers, it is likely that use of those markers, suchas a fingerprint, would be widespread. The widespread use of just one ortwo types of markers increases the likelihood that an unauthorizedperson could, by chance or otherwise, be improperly granted access. Ifan unauthorized person were improperly given access, that individual mayhave access to numerous secured devices or accounts. This is the sameproblem that exists when a person chooses the same password for all hisaccounts or electronic devices and the password is stolen.

U.S. Pat. No. 4,537,484 to Fowler et al. discloses a fingerprint imagingapparatus for use in an identity verification system. The system useslight, which is reflected off the finger through a system of mirrors toa linear photo diode ray. The fingers are rotated mechanically in orderto scan the entire fingerprint.

U.S. Pat. No. 4,544,267 to Shore discloses an identification device thatuses a beam of collimated light to scan the fingerprint. The light beamis then imaged onto a linear ray of photo-responsive devices. Theinformation is processed to provide a set of signals containingfingerprint information.

U.S. Pat. No. 4,699,149 to Rice discloses a device for detecting theposition of subcutaneous blood vessels such as by using the reflectionof incident radiation off of a user's skin. The measured pattern is thencompared with a previously determined pattern to verify the identity ofthe user.

U.S. Pat. No. 4,728,186 to Eguchi et al. discloses another method fordetecting data points on uneven surface such as a finger, namely afingerprint, using a light source illuminating the uneven surfacethrough a transparent plate.

U.S. Pat. No. 4,784,484 to Jensen discloses an apparatus for automaticscanning of a fingerprint using an optical scanner. The user slides hisfinger across a scanning surface and an optical scanning systemgenerates an electrical signal as a function of the movement of thefinger across the optical scanning surface.

U.S. Pat. No. 5,073,950 to Colbert et al. discloses a method andapparatus for authenticating and verifying the identity of an individualbased on the profile of a hand print using an optical scanner.

U.S. Pat. No. 5,077,803 to Kito et al. discloses a fingerprint collatingsystem employing a biological detecting system.

U.S. Pat. No. 5,088,817 discloses an apparatus for detecting andidentifying a biological object by projecting a light beam onto theobject and detecting the reflective light using an optical detector. Thechange in the wave length characteristics of the light beam can becompared to a previously determined pattern.

U.S. Pat. No. 5,230,025 discloses a system for generating datacharacteristics of a rolled skin print using an optical device that canconvert reflective light beams into an electronic signal and generatedigital data representative of the image of the skin print.

U.S. Pat. No. 5,335,288 to Faulkner discloses a biometric measuringapparatus that uses silhouette and light images to measure a person'shand features. The features are converted to electronic data and storedand later compared for identification purposes.

Some biometric authentication systems combine biometric measurementswith conditioned behavior such as signature writing styles and voicepatterns or intonations. For example, U.S. Pat. No. 5,103,486 to Grippeydiscloses a signature verification system utilizing a hand held writingimplement that produces data regarding a person's fingerprint patternand their hand written signature.

Other biometric authentication systems include means for verifyingphysiological activity. These means for verifying physiological activityare primarily to prevent an unauthorized person from using dead tissuesas a means for circumventing the authentication process. For example,U.S. Pat. No. 5,719,950 to Osten et al. discloses a personal biometricauthentication system wherein inherently specific biometric parametersare measured and recognized and at least one non-specific biometricparameter is recognized and compared with physiological norms. Likewise,U.S. Pat. No. 5,727,439 to Lapsley et al. discloses an antifraudbiometric scanner that determines whether blood flow is taking place inthe object being scanned and whether such blood flow is consistent withthat of a living human.

It would therefore be advantageous to provide a method and apparatus forbiometric authentication and activation that does not exclusively relyupon the measurement of superficial anatomical structure and/orbehavioral responses. It would also be advantageous to provide abiometric authentication system that is relatively inexpensive andportable. It would be a further advantage to provide a biometricauthentication system that can use, but does not require, uniquebiometric markers. It would also be advantageous to provide a method andapparatus for biometric authentication that can use a single technologyto measure multiple, varied biometric markers.

BRIEF SUMMARY OF THE INVENTION

The present invention provides a method and apparatus for identificationand authentication using physiological and histological biometricmarkers. The biometric markers of the present invention aresubstantially unique to each person, but not necessarily totally unique.In order to accomplish the present invention, it may be necessary tocharacterize and estimate the parameters of thephysiological/histological markers. Characterization and estimation ofthe parameters of the physiological/histological marker will be referredto hereinafter as “individualization” of the physiological/histologicalmarkers. The biometric markers of the present invention are not merelymeasurements of superficial anatomical structure or behavioral traits,but instead utilize or alternatively include measurements ofphysiological traits of the various systems of the human body and/or arehistological traits associated with tissues of the human body, which areindividualized to enhance the traits' capacity to function as abiometric marker.

The present invention also contemplates the use of individualizedbiometric markers that may not be representative of any particulartraits in and of themselves, but are a composite of variousindividualized physiological and/or histological traits. While thebiometric markers of the present invention may be entirely unique toeach person, markers that are not entirely unique but that aresubstantially unique may be used in the individualization and subsequentauthentication process. This is made possible, in part, by theindividualization system enclosed. In using substantially uniquebiometric markers, the present invention also allows a wide variety ofbiometric characteristics to be employed in a relatively compact andinexpensive device. The present invention employs an individualizationmethod for use with biological markers that are substantially uniquethat remain relatively consistent from measurement to measurement andthat preferably are capable of being measured without physicallyinvasive procedures.

The present invention provides an efficient method for employinginternal biometric markers. These internal markers can easily be used inconjunction with other biometric techniques to improve the layeringtechnique. The layering technique can enhance the security capabilitiesof the present invention. Layering is a technique, which employs the useof more than one biometric marker for authentication. For example, themethod of the present invention works to greatly simplify themeasurement and authentication process, thereby making it more practicalto employ layering techniques.

The use of physiological and histological markers allows the devices inwhich such a biometric system is used to be both secure and readilymanufactured and marketable. The simplicity of these devices is due ingreat measure to the method of individualization that allows themeasured waveform to be converted into a biometric marker. Because ofthe variety of ways in which the physiological markers can be measuredand individualized using the present invention, a variety ofmeasurements can be taken in the system, allowing for greaterflexibility and variability in the markers used and design of thedevice. Contrary to the current trend in the biometric industry, thepresent invention does not limit the types of markers used tosuperficial anatomical structure or complex behavioral activity, andthus both simplifies and expands the potential applications for internalmarks.

Other physical traits can be used for biometric authentication inconjunction with the individualization of a heartbeat. Where a biometricmarker is measured using a signal that passes through these tissues, thetissues may have characteristics that affect the resulting signal orwaveform characteristics that are substantially unique to each person.In a preferred embodiment of the present invention, a heartbeat waveformis measured using a signal that passes through dermal and subdermaltissues and their associated vasculature and musculature. Through thesetissues the heartbeat of the user is measured and then individualized.The present invention provides for the use of specific histologicaltraits of various human tissues, such as epithelial tissue, connectivetissue, muscle tissue, and nervous tissue.

For example, the depth of the various layers of epithelial tissue from agiven point on the skin surface may be a substantially uniquehistological trait that can be used as a biometric marker in conjunctionwith an individualized heartbeat. The density of a particular kind ofconnective tissue, such as bone density, may be a substantially uniquehistological trait that can be employed in a biometric authenticationsystem. Likewise, the light absorption characteristics of skin tissuecould be a substantially unique histological trait.

In the same way that histological markers increase both themarketability and security of biometric systems, the physiologicallybased biometric markers of the present invention provide similaradvantages. Specifically, when properly individualized, the heartbeatwaveform provides physiological markers that do not require the scanningor mapping of anatomical structure. Neither do the heartbeat waveformmarkers require the analysis of volitional acts, as are required withvoice or signature analysis. The heartbeat is a non-volitional,physiological process that occurs in the body. A physiological marker,such as a heartbeat waveform marker, could also include or be combinedwith other physiological processes. A heartbeat waveform marker could beassociated with a physiological marker related to a different system,such as, the integumentary system, the skeletal system, the muscularsystem, the pulmonary system, the respiratory system, the circulatorysystem, the sensory system, the nervous system, the digestive system,the urinary system, the endocrine system, and/or the reproductivesystem. Included in the physiological biometric markers are thoseactivities associated with the various physiological systems that occurautomatically or, in other words, are non-volitional. All of thesesystems and related subsystems provide traits that can be measured in avariety of ways to provide additional substantially unique biometricmarkers for the present invention and may in some cases beindividualized with the heartbeat markers.

Physiological and histological biometric markers, whether related to theheartbeat or not, may be measured in common units such as spacialmeasurements of length, area, and volume. Frequency is also another typeof measurement that can be practically applied to histological andphysiological biometric markers. However, the present invention providesfor the monitoring of these biometric markers in a variety of otheradditional ways. The relative motion of particles and fluids can bemeasured in terms of velocity, acceleration, volumetric flow rate orangular velocity, and angular acceleration. Physical interaction such asforce, surface tension, pressure, viscosity, work, and torque are otherpossible measurements.

The physiological and histological markers may also be based upon energyor heat related characteristics such as power, heat quantity, heat flux,volumetric heat release, heat transfer coefficient, heat capacity, andthermal conductivity. Likewise, measurements, such as electric quantity,electromotive force, electric field strength, electric resistance, andelectrical capacities, could provide biometric markers, depending uponthe tissue or physiological process being monitored. Magnetic relatedcharacteristics, such as magnetic flux, induce, magnetic permeability,magnetic flux density, magnetic field strength, and magneto-motive forcecould be used. Other potential measurements may include luminous flux,luminance, illumination, radio nucleotide activity, radioactivity,temperature, and absorbed dose and dose equivalent, and amount ofsubstance (mole).

In one preferred embodiment, an infrared light transmitter transmits anIR signal into a person's finger when the finger is placed on thetransmitter. The signal transmitter is activated and a signal is emittedfrom the signal transmitter and is transmitted into the dermal andsubdermal tissues of the person's finger. The signal is partly absorbedand reflected by the dermal and subdermal tissues. The reflected signalis received by a signal receiver and transmitted through receiving wiresto a chip where the received signal or features of the signal areindividualized. The signal may then be sufficiently unique to act as abiometric identifier or may be analyzed to provide extracted features ofthe signal that can be used as a biometric marker such as a digitalheartbeat waveform. The biometric identifier is then stored for futureuse in authenticating the person.

Individualizing of the captured signal is accomplished using the methodof the present invention. The process of individualizing the signaltypically comprises capturing and recording a number of signals,estimating particular univariate and multivariate features of thesignals, individualizing the measurements of the features andcalculating probabilities for measurements of the feature. Morespecifically, one embodiment of the present invention comprises thesteps of recording and saving the signal, measuring particular featuresof the signal, calculating the average of each measured feature,subtracting each measurement from the average to yield a centroid value,then, dividing each centroid by the standard deviation as calculatedusing the individualization set, determining the probability of theresulting figure using a distribution calculation and comparing theprobability to the minimum value established.

Correlations between features are also calculated and compared to athreshold value. Correlation values above a specified threshold thenqualify the multivariate characteristic for inclusion in authentication.The multivariate parameters of centroid, standard deviation andcovariance are used to calculate the multivariate probability forcomparison to the minimum value established.

BRIEF DESCRIPTION OF THE SEVERAL VIEWS OF THE DRAWINGS

The foregoing and other objects and features of the present inventionwill become more fully apparent from the following description andappended claims, taken in conjunction with the accompanying drawings.Understanding that these drawings depict only typical embodiments of theinvention and are, therefore, not to be considered limiting of itsscope, the invention will be described and explained with additionalspecificity and detail through the use of the accompanying drawings inwhich:

FIG. 1 illustrates various features of a waveform;

FIG. 2 illustrates a graph showing a strong bivariate relationship;

FIG. 3 illustrates a graph showing a weak bivariate relationship;

FIG. 4 is a table of heartbeat wave form features;

FIG. 5 is a table of heartbeat waveform features; and

FIGS. 6 a and 6 b depict a flow diagram of a processing method forindividualizing heartbeat waveform features of a user and using theindividualized features to authenticate the user.

DETAILED DESCRIPTION OF THE INVENTION

It will be readily understood that the components of the presentinvention, as generally described and illustrated in the figures herein,could be arranged and designed in a wide variety of differentconfigurations. Thus, the following more detailed description of theembodiments of the system and method of the present invention, andrepresented in FIGS. 1 through 3, is not intended to limit the scope ofthe invention, as claimed, but is merely representative of the presentlypreferred embodiments of the invention. The presently preferredembodiments of the invention will be best understood by reference to thedrawings.

The preferred embodiment of the present invention monitors the actualwaveform of the heartbeat and retains certain features or attributesassociated with that waveform for use in individualization andauthentication. For example, the position on the upslope of theheartbeat waveform having the fastest rate of change slope can berecorded and various attributes of that position can be noted. Theamplitude of that position, its position from the center of the pulseand amplitude of the actual beat relative to the position can all bemeasured and recorded. Thus, multiple quantitative features can beextracted from a single characteristic of a waveform.

All of the heartbeat waveforms share a number of standard features thatcan be used as reference points for other measurements. For example, allheartbeat waveforms can be divided into two distinct peaks. As part ofthe individualization process, the heartbeat waveform can be analyzedrelative to the two peaks. Various parameters associated with waveformpeaks include, but are not limited to, the differences between the twopeak amplitudes, the differences between the two peak rate of changes,the relative position of the dicrotic notch, how deep the notch is, howfar the dicrotic notch is from a zero point—a reference point, and howfar it is from the center of one of the peak's, where the peak of thedicrotic notch is located along the horizontal, and the position of thevarious peaks from the center of the waveform and from the center of theother peak. Often several features can be extracted out of the waveformto serve in the individualization process.

In another example, shown in FIG. 1, various features of the waveformare monitored, such as peaks in the waveform, for quadratic and linearcomparison. At the peak of the heartbeat, the waveform can be analyzedto show a quadratic fit. The quadratic term and the linear term of thequadratic that most closely correspond to the curve across the top ofthat heartbeat are potential features of the waveform that can be usedfor identification. Likewise, other features shown in FIG. 1 as well asthose listed in Table A may prove useful in using the waveform as abiometric marker.

In a preferred embodiment, a total of 25 features are extracted out of awaveform to create a list of 25 parameters, each parameter representinga different unique feature for a particular person's heartbeat waveform.In addition to the selected heartbeat waveform parameters, otherinternal biometric features which are not related to a heartbeatwaveform can be included in the list of parameters used inidentification. For example, a measurement of the skin's lightconductance may not be related to the heartbeat waveform and is adifferent kind of parameter, but light conductance can be easilymeasured in conjunction with the capture of the heartbeat waveform.These various features are ideally measured at the same time and cancreate very powerful identification multipliers since the features mayvary over a wide range of individuals.

In order to individualize an internal biometric identifier such as aheartbeat waveform, the biometric must be read and recorded at leastonce. In order to assure an accurate biometric, it is preferable to takemore than one reading of the biometric for purposes ofindividualization. In one preferred embodiment 30 heartbeats were takenand monitored to do the individualization for each person beingidentified. In another preferred embodiment, a hundred heartbeats wereused. In capturing a good sample, it is preferred to take as manysamples as is possible. However, taking a large number of samplewaveforms takes time and using an extended period of time toindividualize the waveform may be impractical.

Having collected various heartbeat waveforms from a person anddetermined various feature's measurements for each waveform, a table ofextracted waveform features measurements can be created. The informationin the table is used to individualize the waveforms of the person fromwhom the measurements were collected into a biometric authenticator.

The first step in the individualization process is determining the meanvector of the measured features in the table. For each feature on thetable, the average of all the samples of that feature is calculated andthen the average of that feature measurement is subtracted out from theactual measurement of the feature. The difference yields a value calledthe “centroid value” or “centroid vector” of the feature measurement.

Next, the standard deviation for each feature measurement is calculated,to show the degree statistical variation the waveforms have amongthemselves. Where there is little fluctuation or variation in a measuredfeature of the waveforms, the feature is relatively consistent and maybe a good authenticating feature and the standard deviation is low. Ifthere is significant variation in the waveform, the standard deviationis high.

Next, each of the measured features is subjected to a probabilitycalculation. In one embodiment, using the centroidal information, theprobability that a particular sample would exist given the range ofmeasurements taken for that particular feature is determined. A validsample is one that falls within a desired range of measurements. For anygiven sample, the probability calculation determines how closely thatfeature's measured value corresponds to the measurements of that samefeature on other waveforms from that person. Where the measurements forthe features of two waveforms are consistent and close together, therange of values for the measurement and related probabilities for theoccurrence of that value can be determined. A subsequent measurementthat shows up within that a range of values that have a high probabilityof occurrence is a “valid” measurement. In other words, there is a goodprobability that the subsequently generated waveform could come from theperson whose waveform generated the initial data set. In one preferredembodiment, the probability for each measurement is calculated using a Tdistribution and the centroid value and the standard deviation.

Before running the T-distribution, it is necessary to take into accountthe fact that for some of the features, the variation can be very rare,while for others, variations could be quite common. In order toindividualize the values, the centroid value for each measurement isdivided by the standard deviation. By individualizing of each one of thedata samples, all the data samples will be in the same standard setregardless of the feature.

After the data are individualized, they are used to carry out the Tdistribution to generate corresponding probabilities for the measuredvalues. Using these probability figures, a “threshold” value for eachparticular feature is determined, that is, the lowest acceptableprobability value is determined. In one preferred embodiment the minimumunivariate value is used as the threshold for determining whether themeasurement taken of a particular feature is considered within the rangeof acceptable variance or is outside the acceptable range. The valuescalculated from an individual for whom the waveform is beingindividualized should fall above the minimum univariate value.

Obviously, it is possible that two people will have one or more waveformfeatures so similar that the values taken from one will match orcorrespond to the other. If only one feature of the waveform weremeasured and individualized for the purposes of biometricauthentication, then there is a strong possibility that two differentpeople would have similar measured values for their “biometricprofiles.” In order to reduce the likelihood of such false positives,calculations are carried out for multiple features creating a table ofunivariates with corresponding minimum values. These minimum values canbe compared or combined to yield an overall or global minimum univariateevent called the total minimum.

The various data collected from the waveform and generated from thecalculations performed on the collected waveform create a data setunique to person from whom the data were gathered. By combining theprobabilities to create a univariate threshold, the present inventioncreates a unique biometric marker from a data set taken from an internalbiometric marker.

In addition to univariate processing, the present invention alsoprovides for bivariate processing. Bivariate processing begins with adetermination of whether a relationship exists between the values foreach of the features. For example, a determination must be made as towhether there is a correlation between feature one and feature two. Ifone feature represents amplitude of the waveform and a second feature isthe amplitude of the dicrotic notch, and the two features measurementscorrespond in some reliable way, (e.g., the depth of the dicrotic notchis deep when the width of the pulse is narrow) the relationship can beused to further individualize the waveform to function as a biometricmarker.

If there is a strong relationship between two univariate values, alinear correlation may exist and be used to individualize the waveform.The linear relation between the features can be shown graphically bytaking the measured values and plotting it with the other related,measured values. Where the relationship between the values is strong,the graph has a cigar shape, as shown in FIG. 2, but where therelationship is not as strong, the graph would has a round shapeinstead, as shown in FIG. 3. In order to determine how well the featurescorrelate with each other, each possible pairwise combination offeatures is evaluated. In the preferred embodiment having 25 featuresthere are 300 possible bivariates. All 300 are analyzed for purposes ofindividualization.

In order to evaluate the degree of correspondence between two variables,the centroid value for each measurement of each measured feature isdivided by its standard deviation and then multiplied together. Theresulting values are summed and the summation is divided by the degreesof freedom (a value one less than the number of samples in thesummation).

By comparing the bivariate combinations, a determination can be made asto which bivariates have the highest degree of correspondence. In somecircumstances a user may have two different univariate values thatindividually are too inconsistent to function as validators, but show astrong correlation between their otherwise inconsistent values. Thesetwo “individually weak” univariates can be combined to form a strongbivariate. Using the summation calculation above, one can determinewhich bivariates have a strong correspondence. Bivariates thatcorrespond exactly have a summation value of one. Where there is nocorrespondence at all, the summation value is zero. Bivariates with acorrespondence close to one are typically the most helpful inindividualizing the waveform and in subsequent authentication.

Having performed summation on a selected group of bivariates, athreshold value between zero and one is applied to the summation of thegroup. The selection of the threshold value is determined by balancingthe need for highly correlating bivariates versus the need to employ alarge number of bivariates. For example, a threshold value of 0.8 may beselected for a given group of bivariate. If the summation value of aparticular bivanate is less than 0.8 then that bivanate value is notincluded in the biometric individualization, if it is above 0.8, thenthe bivariate correlates to an acceptable degree and the bivariate isincluded in the biometric individualization. Each one of thesebivariates having a summation that falls above the 0.8 threshold valueis electronically flagged and stored to be used as part of theindividualization.

The number of bivariates that will be used in the individualization willdepend upon the threshold values chosen and also on the individual forwhom the individualization is done. Likewise the bivariates chosen willchange from person to person because the bivariate correlation willchange; some of the bivariates will work better for some people thanthey will for others. However, after the bivariates are established fora given person, the same bivariates are used for subsequentauthentication.

Next the probability of the bivanates are calculated. In order todetermine the probability that two bivanates properly authenticate theuser, the Mahalanobis distance of the each of the bivariates iscalculated. Determining this Mahalanobis distance involves calculatingthe average of the bivanates and determining the difference of eachvalue from the average. Then using a cumulative gamma distributioncalculation for each of the measured Mahalanobis distances, theprobability that a certain bivariate represents the authentic user iscalculated.

Comparing all of the univariate and bivariate probabilities, the minimumprobability minimum value for all is obtained. The minimum probabilitycan be used as threshold or basis for indicating identity between apresent user's biometric “signature” and the signature as initiallyindividualized. Alternatively, the probability value is just above theminimum value or some other probability value can be used.

All the information gathered and calculated by these various processescan be stored for use in individualization, calculations andverifications. The data is stored as the processes are completed. Forexample, for each feature, the measured value, the average, thecentroid, the standard deviation, the minimum univariate T distribution,and the bivariates gamma distribution are stored in the device for lateruse.

In summary, the process of individualizing a person's heartbeat waveformunder normal operations comprises the steps of capturing and saving theheartbeat signal, measuring particular features of the signal,subtracting each measurement from the average to yield the centroid,then, dividing each centroid by the standard deviation as calculatedusing the individualization set, determining the probability of theresulting figure using a distribution calculation and comparing theprobability to the minimum value established. If the values are withinthe limits established by the individualization set, the person isauthenticated. Using the data from the signal, a set of highlycorrelating bivariates is defined and distribution calculations areperformed to determine the probability of the measured bivariates. Thebivariate probabilities are also used in individualization andsubsequent authentication.

One problem in making such authentication is knowing how to establish aminimum value for the univariate and bivariate values. In onealternative embodiment the minimum probability is used. However, inorder to reduce the chances that an anomalous reading will be includedin the individualization, a preferred embodiment uses a higher orderedminimum, such as a second or third ordered minimum. Naturally the higherup in this ordered sequence the minimum value is, the more likely thevalue will yield false negative.

In one embodiment of the present invention some of the features areglobally weighted more than others during authentication. A particularfeature, such as the slope of the dicrotic notch, may be considered moreor less reliable as an identifier and thereby may be given more or less“statistical” weight in the individualization process. Likewise, thecorrelation between two measurements for a particular feature or thecorrelation between two different features may be stronger than forother features and be weighted accordingly. Some of the features maycarry much more significant information than other features.

During the initial individualization process, it is preferable if theheartbeat signal captured is the first full heartbeat that occurs afterthe user has placed his finger on a device. The process preferably takesone second or less. In one embodiment, the biometric measuring hardwareis primarily an analog circuitry and takes about one-half second beforeit is ready to begin sampling a user's heartbeat. Because of hardwarelimitations in some embodiments, heartbeat signal capture within two ormore heartbeats is preferable.

The captured waveform is characterized and measured using variouspredetermined features of the waveform signal from an authenticateduser. Based on these preselected features and parameters,individualization data sets are prepared, establishing parameters foreach one of the features. The parameters for the features are then usedto evaluate heartbeat signals during subsequent authentication. In otherwords, the present invention determines the likelihood or probabilitythat a particular biometric waveform was generated by the authenticateduser. Because the waveform measurements are never exactly the same fromsample to sample, the present invention evaluates the probability thattwo waveforms come from the same person. For each authenticated waveformdata sets, a threshold probability value is established for the purposesof authenticating the signal and for use of the signal as a biometricidentifier. The threshold value is used to determine whether a specificuser's waveform is considered authentic. The threshold may be any valuethat reflects the desired balance between consistency and selectivity.

One advantage of this embodiment of the present invention is that ittakes into account that on occasion a typically consistent feature in auser's heartbeat waveform will be inconsistent with its usual pattern.The present invention is able to take such irregularities into accountand still provide an authenticating process. For example, if a waveformhas an abbreviated peak for some reason, that particular feature thatrepresented the crown of the peak could be lost or unavailable forpurposes of authentication. However, with the waveform individualized inaccordance with the present invention, there are other features in theindividualization set that are still reliable and those other featurescan accurately authenticate the user. An irregular feature may lower theprobability of a positive authentication, but might not lower theprobability to the point of giving a false negative. The user may be“recognized” and authenticated from the other features.

After individualization, it may be determined that some of the measuredfeatures of a user's waveform are not helpful in the identificationprocess. In other words, for reasons of inconsistency or for otherreasons some of the features may not provide information that can beincluded in individualizing the waveform. In one embodiment of thepresent invention, features which are not helpful in theindividualization process are thereafter not determined or measuredduring any subsequent authentication procedures for that user. Inanother embodiment, the features are determined and measured but are notincluded in calculations or analysis of subsequent waveforms forauthentication. By “turning off” the less helpful features, thebiometric marker is more succinctly defined. During authentication, thestored memory of a device contains the user's individualization waveformset and only evaluates those particular bivariate and those particularfeatures. Likewise, in another embodiment, in a pre-selection processbased upon the relative weights and probabilities of various univariatesand bivariates, certain features are flagged as being features that mostclearly authenticate an individual. The flagged features are used as theauthenticating features for the individual.

In one example the device authenticates a user based upon the user'sselecting a user name or identification that is associated with aparticular individualized waveform. In particular, the user activatesthe device which then prompts the user to select from among severalregistered users, or asks the user to identify himself. The user enterssome form of identification recognizable to the device, such as enteringor selecting a name, social security number or password, and the devicerecalls from machine memory the individualized waveform associated withthe identifying entry. The machine then takes the waveform of the userand compares it to the waveform recalled from memory. If the waveformscorrespond appropriately, the user is authenticated.

Alternatively, a device may be designed to provide access to twentyauthorized users. The twenty users would each go through theindividualization process to determine their individual templates orwaveforms and a chip inside the device would store the waveforms or aremote database could store it and the device could access the database.The device then reads the waveform of potential users and interrogatesthe chip to compare the new waveform to the twenty waveforms stored inthe device. If there is a match, the user is granted access. By the samesystem, the device can determine and keep track of who has accessed thedevice.

If a particular feature does not match the individualization values,this lowers the probability of generating a true positive. However, forthe particular value there is also a range of probable values and basedon these calculated probabilities.

The method of the present invention is carried out by being programmedin machine readable instructions, such as is common with computersoftware, and implemented to act on a computer system. The machinereadable instructions may be integrated into a memory chip, or may bestored as data on a portable storage medium such as a floppy disk or CDROM. The method may likewise be carried out using a signal transmittedover a wired or wireless network where, the signal carries the machinereadable instructions.

Referring now to FIGS. 6 a and 6 b, flow diagrams depict a processingmethods 600 for individualizing heartbeat waveform features of a userand using the individualized features to authenticate the user.Processing methods 600 may be implemented on a computing system, and themethod steps may be implemented as computer readable instructions storedon a computer readable storage media.

At step 605 the method may be initialized to enroll a new user. At step610, a plurality of electronic signals corresponding to an internalbiometric of an individual may be received. In embodiment 600, theseelectronic signals may correspond to a heartbeat waveform of anindividual.

At step 620, one or more pre-selected heartbeat waveform features may bemeasured. The measured features may include, but are not limited to, theheartbeat waveform features listed in Table A of FIGS. 4 and 5.

At step 630, univariate authentication features of the receivedwaveforms may be determined and individualized. In one embodiment,individualizing univariate features may comprise calculating statisticalproperties of the measured waveform features. First, an average or meanvalue of each feature measured at 620 may be calculated. This may bedone by summing all of the feature measurements of 620 and dividing theresult by the number of signals received at 610. After calculating themean of each feature, a standard deviation value for the feature may becalculated. The standard deviation of a feature may show the degree ofstatistical variation of the feature among the waveforms received at610. The standard deviation of a feature may be calculated by summingthe squares of the difference between each feature measurement and thefeature's mean and dividing the result by the number of waveformsreceived at 610.

The standard deviation value of each feature calculated may be used todetermine whether a particular feature will be a good authenticator. Forexample, as discussed above, where there is little variation in ameasured feature, the feature may be said to be relatively consistentand, as such, may be a good authentication feature. However, a largestandard deviation in a feature may indicate that the feature is notconsistent, indicating that the feature may be less effective forauthentication. In one embodiment, method 600 may only use featureshaving a standard deviation value below a pre-determined threshold.

The features measured at step 620 may then be “normalized” at step 630by storing and associating the standard deviation with its correspondingfeature. Then, when the feature is used to authenticate a user, thestandard deviation value may be used to scale or weigh a particularfeature measurement against its expected value. This may individualizeeach of the feature measurements into the same standard set regardlessof the variation of the feature. This process may be considered asapplying a “weight” to each feature depending upon the feature'sdeviation and/or effectiveness as an authenticator.

At step 640, bivariate features of the waveform signals received at 610may be determined. As discussed above, bivariate features may be used asbiometric authenticators. The process of identifying possible bivariatefeatures may begin with a determination of whether a relationship existsbetween pairs of the features measured at step 620. For instance, adetermination may be made as to whether there is a statisticalcorrelation between the waveform amplitude feature and dicrotic notchamplitude feature. If there is a correlation, the relationship may beused as an authenticating feature of an individual's heartbeat waveform.

At step 640, the method may identify correlated features using linearcorrelation. A linear correlation may be shown graphically by taking themeasured feature values and plotting them with the other, possiblyrelated, feature values. Where the relationship between the variables isstrong, the graph may take on a cigar shape as shown in FIG. 2. Wherethe relationship is not as strong, the graph may have a round shape asshown in FIG. 3. In order to evaluate the degree of correspondencebetween two variables, a centroid value for each feature measurement maybe divided by its standard deviation and them multiplied together. Asdiscussed above, a centroid value is the difference between a featuremeasurement and the mean value of the feature measurement. The resultingvalues may be summed together and divided by the degrees of freedom (thedegrees of freedom is one less than the number of samples in thesummation). As with univariate values, the degree of correspondence mayact to weigh the relative efficacy of a particular bivariate for thepurposes of authentication.

Although only bivariate feature correlations are discussed, it would beunderstood by one skilled in the art that any number of features couldbe combined to create other multivariate authenticators, such astrivariate, quadvariate, or the like.

At step 650, a user may be authenticated based on the individualized andweighted univariate and bivariate features determined at steps 605 and640.

At step 460, a heartbeat waveform may be received from a user to beauthenticated, and the heartbeat waveform features identified at andindividualized at steps 620 to 640 may be measured.

At step 670, the features measured at 660 may be subjected to aunivariate probability calculation to determine the probability that thefeatures measured at 660 originated from the user. This probability maybe calculated using a centroid value, standard deviation, and a Tdistribution. The centroid value of step 670 is calculated by taking adifference between the measurement of step 660 and the mean value forthe feature (calculated at 630). The centroid may them be divided by thestandard deviation for the feature (the weight of the feature) to“standardize” the feature relative to other features having greater orsmaller standard deviation values. A T distribution may then be appliedto generate a probability that the measured value corresponds to theindividual enrolled at 605-640. The calculation of step 670 may beperformed for all of the features measured at step 660 or for a sub-setof the features measured at step 660.

At step 680, each of the bivariate features, if any, determined andindividualized at step 640 may be measured against the featuremeasurements of step 660 to determine bivariate probabilities. Theseprobabilities may be calculated using a Mahalanobis distance betweeneach of the bivariates. As discussed above, a Manalanobis distance maybe calculated by calculating the average of the bivariates anddetermining the difference of each value from the average. Thedifferences may be divided by the standard deviation of each bivariate(bivariate weight) in order to “standardize” the difference. Thedifferences may then be applied to a cumulative gamma distributionresulting in a probability that the bivariate originated from aparticular user. As in step 660, a probability may be calculated foreach of the bivariate features individualized at step 640 for a sub-setof the bivariate features of step 640.

At step 690, the probabilities calculated for univariate features atsteps 670 and bivariate features at step 680 may be combined todetermine an overall probability that the waveform received at 660 wasgenerated by the user enrolled and individualized at steps 605 to 640.This determination may be made on a feature-by-feature basis where theuser may be rejected if any one univariate or bivariate feature divergesfrom the expected value by some threshold value or, alternatively, thedetermination may be based upon an overall probability. An overallprobability may be determined by combining the probabilities calculatedat steps 670 and 680 into a single value. The overall probably may thenbe compared against a threshold value to determine whether the usershould be authenticated.

At step 700, the combined probability or probabilities determined atstep 690 is compared against one or more threshold values. As discussedabove, if the probability/probabilities are within a pre-determinedthreshold, the user may be authenticated. If not, the user may not beauthenticated.

If the user is authenticated, the flow may continue to step 710. At step710, the user may be informed that authentication was successful. Inaddition, the feature measurements obtained at step 660 may be includedin individualization process of steps 630 and 640. In this way, themethod may adapt to changes in the user's heartbeat waveform over time.The flow may then continue to step 730 where the method may terminate.

If the user was not authenticated, the flow may continue at step 720. Atstep 720, the user may be informed that authentication was notsuccessful. The feature measurements obtained at step 660 should not beincluded in the individualization process if authentication fails. Theflow may then continue to step 730 where the method may terminate.

1. A method in a computer system for individualizing a heartbeat signalfor use as a biometric marker comprising the steps of: acquiring aplurality of electronic heartbeat signals from an individual in anelectronic signal form using an optical sensor; for each electronicheartbeat signal, measuring, a plurality of pre-selected heartbeatwaveform features to generate corresponding measurements; weighting thepre-selected heartbeat waveform features; providing a differentstatistical weight for each pre-selected heartbeat waveform feature; andauthenticating an individual based on the weighted pre-selectedheartbeat waveform features.
 2. The method of claim 1 furthercomprising: for each electronic heartbeat signal, measuring anadditional pre-selected heartbeat waveform feature to generate acorresponding additional measurement; and preventing the weighting ofthe additional pre-selected heartbeat waveform feature in thestatistical analysis.
 3. The method of claim 1 further comprising:individualizing the measurements of the pre-selected heartbeat waveformfeatures; and calculating probabilities for the measurements.
 4. Themethod of claim 3, wherein individualizing the measurements comprises:subtracting each corresponding measurement from an average value of themeasurements to yield a centroid value for each pre-selected heartbeatwaveform feature, dividing each centroid value by a standard deviationto yield a quotient value, determining a probability of the quotientvalue using a distribution calculation, and selecting a thresholdminimum probability.
 5. The method of claim 4, wherein calculatingprobabilities for the measurements comprises calculating a probabilityof divergence for each measurement using the quotient value.
 6. Themethod of claim 5, wherein calculating the probability of divergenceusing the quotient value includes using the quotient value in aT-distribution analysis.
 7. The method of claim 1, further comprising:calculating an average for each of said pre-selected heartbeat waveformfeatures from said measurements; subtracting the average from eachcorresponding measurement to yield a centroid value; calculating astandard deviation for each pre-selected heartbeat waveform feature;dividing the corresponding centroid value by the standard deviation foreach pre-selected heartbeat waveform feature; and calculating aprobability of divergence for each measurement corresponding to eachpre-selected heartbeat waveform feature.
 8. The method of claim 7further comprising: for each electronic heartbeat signal, measuring anadditional pre-selected heartbeat waveform feature to generate acorresponding additional measurement; and preventing the weighting ofthe additional pre-selected heartbeat waveform feature in thestatistical analysis.
 9. The method of claim 1, wherein the pre-selectedheartbeat waveform features include univariate features of a heartbeatwaveform.
 10. The method of claim 9, wherein the pre-selected heartbeatwaveform features include multivariate features of a heartbeat waveform.11. The method of claim 1, wherein a pre-selected heartbeat waveformfeature is a position of a dicrotic notch.
 12. The method of claim 1,wherein a pre-selected heartbeat waveform feature is a differencebetween two peak pressure amplitudes.
 13. The method of claim 1, whereina pre-selected heartbeat waveform feature is a difference between twopeak pressure change rates.
 14. The method of claim 1, wherein apre-selected heartbeat waveform feature reflects how far a dicroticnotch is from a zero point.
 15. The method of claim 1, wherein apre-selected heartbeat waveform feature is an up slope of a maximum peakpressure.
 16. The method of claim 1, wherein a pre-selected heartbeatwaveform feature is a down slope of a maximum peak pressure.
 17. Themethod of claim 1 further comprising: establishing a thresholdprobability value for each pre-selected heartbeat waveform feature,wherein the threshold value reflects a desired consistency andselectivity.
 18. A computer readable storage medium containinginstructions for controlling a computer system to perform a method toindividualize a heartbeat electronic signal for use in biometricauthentication, the method comprising: acquiring a plurality ofelectronic heartbeat signals from an individual in an electronic signalform using an optical sensor; for each electronic heartbeat signal,measuring one or more pre-selected heartbeat waveform features togenerate corresponding measurements; weighing the pre-selected heartbeatwaveform features; providing a different statistical weight for each ofthe one or more pre-selected heartbeat waveform features; andauthenticating a user using said weighed pre-selected heartbeat waveformfeatures.
 19. The computer readable storage medium of claim 18 wheresaid measurements are made on only one heartbeat waveform feature peracquisition.
 20. The computer readable storage medium of claim 18 wheresaid measurements are made on two heartbeat waveform features peracquisition.
 21. The computer readable storage medium of claim 18 wheresaid measurements are made on a plurality of heartbeat waveform featuresper acquisition.
 22. The computer readable storage medium of claim 18,wherein said method further comprises: individualizing the measurementsof the pre-selected heartbeat waveform features, and calculatingprobabilities for the measurements.
 23. The computer readable storagemedium of claim 22, wherein individualizing the measurements comprises:for each pre-selected heartbeat waveform feature, subtracting eachcorresponding measurement from an average value of the measurements toyield a centroid value, dividing each centroid value by a standarddeviation to yield a quotient value, determining a probability of thequotient value using a distribution calculation, and selecting athreshold minimum probability.
 24. The computer readable storage mediumof claim 23, wherein calculating probabilities for the measurementscomprises calculating a probability of divergence for each measurementusing the quotient value.
 25. The computer readable storage medium ofclaim 24, wherein calculating the probability of divergence using thequotient value includes using the quotient value in a T-distributionanalysis.
 26. The computer readable storage medium of claim 18, whereinsaid method further comprises: calculating an average for each of saidpre-selected heartbeat waveform features from said measurements;subtracting the average from each corresponding measurement to yield acentroid value; calculating a standard deviation for each pre-selectedheartbeat waveform feature; dividing the corresponding centroid value bythe standard deviation for each pre-selected heartbeat waveform feature;and calculating a probability of divergence for each measurementcorresponding to each pre-selected heartbeat waveform feature.
 27. Thecomputer readable storage medium of claim 18, wherein said methodfurther comprises authenticating an individual based on the weightedpre-selected heartbeat waveform features.
 28. The computer readablestorage medium of claim 18, wherein the pre-selected heartbeat waveformfeatures include univariate features of a heartbeat waveform.
 29. Thecomputer readable storage medium of claim 28, wherein the pre-selectedheartbeat waveform features include multivariate features of a heartbeatwaveform.
 30. The computer readable storage medium of claim 18, whereina pre-selected heartbeat waveform feature is a position of a dicroticnotch.
 31. The computer readable storage medium of claim 18, wherein apre-selected heartbeat waveform feature is a difference between two peakpressure amplitudes.
 32. The computer readable storage medium of claim18, wherein a pre-selected heartbeat waveform feature is a differencebetween two peak pressure change rates.
 33. The computer readablestorage medium of claim 18, wherein a pre-selected heartbeat waveformfeature reflects how far a dicrotic notch is from a zero point.
 34. Thecomputer readable storage medium of claim 18, wherein a pre-selectedheartbeat waveform feature is an up slope of a maximum peak pressure.35. The computer readable storage medium of claim 18, wherein apre-selected heartbeat waveform feature is a down slope of a maximumpeak pressure.
 36. The computer readable storage medium of claim 18,wherein said method further comprises: establishing a thresholdprobability value for each pre-selected heartbeat waveform feature,wherein the threshold value reflects a desired consistency andselectivity.
 37. The computer readable storage medium of claim 18,wherein said method further comprises: measuring an additionalpre-selected heartbeat waveform feature to generate a correspondingadditional measurement for each electronic heartbeat signal; andpreventing the weighting of the additional pre-selected heartbeatwaveform feature in the statistical analysis.